Wearable apparatus and method for monitoring posture

ABSTRACT

The present disclosure relates to systems and methods for monitoring posture of a user of a wearable apparatus. In one implementation, the system may include an image sensor configured to capture one or more images from an environment of the user of the wearable apparatus and at least one processing device. The at least one processing device may be configured to analyze the one or more images to determine at least one indicator of the posture of the user, determine feedback for the user based on the at least one indicator of the posture of the user, and cause the feedback to be output to the user.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of United States Provisional Patent Application No. 62/350,060, filed Jun. 14, 2016, which is incorporated herein by reference in its entirety.

BACKGROUND Technical Field

This disclosure generally relates to devices and methods for monitoring posture using a wearable apparatus. More particularly, this disclosure relates to devices and methods for monitoring posture of a user of a wearable apparatus and providing feedback to the user based on the posture.

Background Information

Today, technological advancements make it possible for wearable devices to automatically capture images and store information that is associated with the captured images. Certain devices have been used to digitally record aspects and personal experiences of one's life in an exercise typically called “lifelogging.” Some individuals log their life so they can retrieve moments from past activities, for example, social events, trips, etc. Lifelogging may also have significant benefits in other fields (e.g., business, fitness and healthcare, and social research). Lifelogging devices, while useful for tracking daily activities, may be improved with capability to enhance one's interaction in his environment with feedback and other advanced functionality based on the analysis of captured image data.

Even though users can capture images with their smartphones and some smartphone applications can process the captured images, smartphones may not be the best platform for serving as lifelogging apparatuses in view of their size and design. Lifelogging apparatuses should be small and light, so they can be easily worn. Moreover, with improvements in image capture devices, including wearable apparatuses, additional functionality may be provided to assist users in navigating in and around an environment, identifying persons and objects they encounter, and providing feedback to the users about their surroundings and activities.

Lifelogging devices often track statistics about physical activities of the user, e.g., the number of steps taken, the number of miles run, the number of minutes exercising, etc. Lifelogging devices may then provide feedback based on these statistics. Such feedback, however, is generally both broad and ex post facto. Some feedback, like that related to posture, is best if specific and/or provided in real-time. Therefore, there is a need for apparatuses and methods for monitoring posture of the user of a wearable device, such as a lifelogging apparatus, and providing feedback related thereto.

SUMMARY

Embodiments consistent with the present disclosure provide devices and methods for monitoring posture of a user of a wearable apparatus. For example, a wearable apparatus for monitoring a posture of a user of the wearable apparatus may comprise an image sensor configured to capture one or more images from an environment of the user of the wearable apparatus and at least one processing device. The at least one processing device may be configured to analyze the one or more images to determine at least one indicator of the posture of the user, determine feedback for the user based on the at least one indicator of the posture of the user, and cause the feedback to be output to the user.

In another embodiment, a method for monitoring a posture of a user of the wearable apparatus may comprise capturing, via an image sensor included in the wearable apparatus, one or more images from an environment of the user of the wearable apparatus. The method may further comprise analyzing, via at least one processing device included the wearable apparatus, the one or more images to determine at least one indicator of the posture of the user and determining, via the at least one processing device, feedback for the user based on the at least one indicator of the posture of the user. The method may further comprise causing, via the at least one processing device, the feedback to be output to the user.

Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processor and perform any of the methods described herein.

The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings:

FIG. 1A is a schematic illustration of an example of a user wearing a wearable apparatus according to a disclosed embodiment.

FIG. 1B is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment.

FIG. 1C is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment.

FIG. 1D is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment.

FIG. 2 is a schematic illustration of an example system consistent with the disclosed embodiments.

FIG. 3A is a schematic illustration of an example of the wearable apparatus shown in FIG. 1A.

FIG. 3B is an exploded view of the example of the wearable apparatus shown in FIG. 3A.

FIG. 4A is a schematic illustration of an example of the wearable apparatus shown in FIG. 1B from a first viewpoint.

FIG. 4B is a schematic illustration of the example of the wearable apparatus shown in FIG. 1B from a second viewpoint.

FIG. 5A is a block diagram illustrating an example of the components of a wearable apparatus according to a first embodiment.

FIG. 5B is a block diagram illustrating an example of the components of a wearable apparatus according to a second embodiment.

FIG. 5C is a block diagram illustrating an example of the components of a wearable apparatus according to a third embodiment.

FIG. 6 illustrates an exemplary embodiment of a memory containing software modules consistent with the present disclosure.

FIG. 7 is a schematic illustration of an embodiment of a wearable apparatus including an orientable image capture unit.

FIG. 8 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.

FIG. 9 is a schematic illustration of a user wearing a wearable apparatus consistent with an embodiment of the present disclosure.

FIG. 10 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.

FIG. 11 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.

FIG. 12 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.

FIG. 13 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.

FIG. 14 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.

FIG. 15 is a schematic illustration of an embodiment of a wearable apparatus power unit including a power source.

FIG. 16 is a schematic illustration of an exemplary embodiment of a wearable apparatus including protective circuitry.

FIG. 17 illustrates another exemplary embodiment of a memory containing software modules consistent with the present disclosure.

FIG. 18 is a schematic illustration of another example system consistent with the disclosed embodiments.

FIG. 19 is a schematic illustration of a user wearing the wearable apparatus shown in FIG. 18.

FIG. 20 is a schematic illustration of a point-of-view of the wearable apparatus of FIG. 19 consistent with the present disclosure.

FIG. 21 is a schematic illustration of exemplary visual feedback provided to the user according to a disclosed embodiment.

FIG. 22 is a schematic illustration of exemplary visual feedback provided to the user according to another disclosed embodiment.

FIG. 23 is a flowchart of a method for monitoring posture of and providing feedback to a user according to a disclosed embodiment.

FIG. 24 is a flowchart of a method for analyzing posture indicators according to a disclosed embodiment.

FIG. 25 is a flowchart of a method for monitoring posture of one or more body parts of a user according to a disclosed embodiment.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions or modifications may be made to the components illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope is defined by the appended claims.

Embodiments of the present disclosure may monitor and/or provide feedback related to the posture of a user of a wearable apparatus. For example, systems and methods of the present disclosure may monitor the posture of the user or of an identified body part of the user. Real-time feedback may then be provided to the user based on the monitored posture.

FIG. 1A illustrates a user 100 wearing an apparatus 110 that is physically connected (or integral) to glasses 130, consistent with the disclosed embodiments. Glasses 130 may be prescription glasses, magnifying glasses, non-prescription glasses, safety glasses, sunglasses, etc. Additionally, in some embodiments, glasses 130 may include parts of a frame and earpieces, nosepieces, etc., and one or no lenses. Thus, in some embodiments, glasses 130 may function primarily to support apparatus 110, and/or an augmented reality display device or other optical display device. In some embodiments, apparatus 110 may include an image sensor (not shown in FIG. 1A) for capturing real-time image data of the field-of-view of user 100. The term “image data” includes any form of data retrieved from optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums. The image data may include video clips and/or photographs.

In some embodiments, apparatus 110 may communicate wirelessly or via a wire with a computing device 120. In some embodiments, computing device 120 may include, for example, a smartphone, or a tablet, or a dedicated processing unit, which may be portable (e.g., can be carried in a pocket of user 100). Although shown in FIG. 1A as an external device, in some embodiments, computing device 120 may be provided as part of wearable apparatus 110 or glasses 130, whether integral thereto or mounted thereon. In some embodiments, computing device 120 may be included in an augmented reality display device or optical head mounted display provided integrally or mounted to glasses 130. In other embodiments, computing device 120 may be provided as part of another wearable or portable apparatus of user 100 including a wrist-strap, a multifunctional watch, a button, a clip-on, etc. And in other embodiments, computing device 120 may be provided as part of another system, such as an on-board automobile computing or navigation system. A person skilled in the art can appreciate that different types of computing devices and arrangements of devices may implement the functionality of the disclosed embodiments. Accordingly, in other implementations, computing device 120 may include a Personal Computer (PC), laptop, an Internet server, etc.

FIG. 1B illustrates user 100 wearing apparatus 110 that is physically connected to a necklace 140, consistent with a disclosed embodiment. Such a configuration of apparatus 110 may be suitable for users that do not wear glasses some or all of the time. In this embodiment, user 100 can easily wear apparatus 110, and take it off.

FIG. 1C illustrates user 100 wearing apparatus 110 that is physically connected to a belt 150, consistent with a disclosed embodiment. Such a configuration of apparatus 110 may be designed as a belt buckle. Alternatively, apparatus 110 may include a clip for attaching to various clothing articles, such as belt 150, or a vest, a pocket, a collar, a cap or hat or other portion of a clothing article.

FIG. 1D illustrates user 100 wearing apparatus 110 that is physically connected to a wrist strap 160, consistent with a disclosed embodiment. Although the aiming direction of apparatus 110, according to this embodiment, may not match the field-of-view of user 100, apparatus 110 may include the ability to identify a hand-related trigger based on the tracked eye movement of a user 100 indicating that user 100 is looking in the direction of the wrist strap 160. Wrist strap 160 may also include an accelerometer, a gyroscope, or other sensor for determining movement or orientation of a user's 100 hand for identifying a hand-related trigger or orientation.

FIG. 2 is a schematic illustration of an exemplary system 200 including a wearable apparatus 110, worn by user 100, and an optional computing device 120 and/or a server 250 capable of communicating with apparatus 110 via a network 240, consistent with disclosed embodiments. In some embodiments, apparatus 110 may capture and analyze image data, identify at least one posture indicator present in the image data, and perform an action and/or provide feedback to a user 100, based at least in part on the identification of the at least one indicator. In some embodiments, optional computing device 120 and/or server 250 may provide additional functionality to enhance interactions of user 100 with his or her environment, as described in greater detail below.

According to the disclosed embodiments, apparatus 110 may include an image sensor system 220 for capturing real-time image data of the field-of-view of user 100. In some embodiments, apparatus 110 may also include a processing unit 210 for controlling and performing the disclosed functionality of apparatus 110, such as to control the capture of image data, analyze the image data, and perform an action and/or output a feedback based on at least one posture indicator identified in the image data. According to the disclosed embodiments, at least one posture indicator may include a gesture performed by user 100 involving a portion of a body part of user 100. Further, consistent with some embodiments, at least one posture indicator may include a visual indicator of posture within the image, e.g., a location of a knuckle of the user indicating a posture of the user's hand, a contour of a finger of the user indicating the shape of the user's grip, an outline of a neck indicating the position thereof. Additionally, in some embodiments, apparatus 110 may include a feedback outputting unit 230 for producing an output of information to user 100. Feedback outputting unit 230 may include one or more vibration devices, e.g., a vibration motor, as described in greater detail below.

As discussed above, apparatus 110 may include an image sensor 220 for capturing image data. The term “image sensor” refers to a device capable of detecting and converting optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums into electrical signals. The electrical signals may be used to form an image or a video stream (i.e. image data) based on the detected signal. The term “image data” includes any form of data retrieved from optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums. Examples of image sensors may include semiconductor charge-coupled devices (CCD), active pixel sensors in complementary metal-oxide-semiconductor (CMOS), or N-type metal-oxide-semiconductor (NMOS, Live MOS). In some cases, image sensor 220 may be part of a camera included in apparatus 110.

Apparatus 110 may also include a processor 210 for controlling image sensor 220 to capture image data and for analyzing the image data according to the disclosed embodiments. As discussed in further detail below with respect to FIG. 5A, processor 210 may include a “processing device” for performing logic operations on one or more inputs of image data and other data according to stored or accessible software instructions providing desired functionality. In some embodiments, processor 210 may also control feedback outputting unit 230 to provide feedback to user 100 including information based on the analyzed image data and the stored software instructions. As the term is used herein, a “processing device” may access memory where executable instructions are stored or, in some embodiments, a “processing device” itself may include executable instructions (e.g., stored in memory included in the processing device).

In some embodiments, the information or feedback information provided to user 100 may include time information. The time information may include any information related to a current time of day and, as described further below, may be presented in any sensory perceptive manner. In some embodiments, time information may include a current time of day in a preconfigured format (e.g., 2:30 pm or 14:30). Time information may include the time in the user's current time zone (e.g., based on a determined location of user 100), as well as an indication of the time zone and/or a time of day in another desired location. In some embodiments, time information may include a number of hours or minutes relative to one or more predetermined times of day. For example, in some embodiments, time information may include an indication that three hours and fifteen minutes remain until a particular hour (e.g., until 6:00 pm), or some other predetermined time. Time information may also include a duration of time passed since the beginning of a particular activity, such as the start of a meeting or the start of a jog, or any other activity. In some embodiments, the activity may be determined based on analyzed image data. In other embodiments, time information may also include additional information related to a current time and one or more other routine, periodic, or scheduled events. For example, time information may include an indication of the number of minutes remaining until the next scheduled event, as may be determined from a calendar function or other information retrieved from computing device 120 or server 250, as discussed in further detail below.

Feedback outputting unit 230 may include one or more feedback systems for providing the output of information to user 100. In the disclosed embodiments, the audible or visual feedback may be provided via any type of connected audible or visual system or both. Feedback of information according to the disclosed embodiments may include audible feedback to user 100 (e.g., using a Bluetooth™ or other wired or wirelessly connected speaker, or a bone conduction headphone). Feedback outputting unit 230 of some embodiments may additionally or alternatively produce a visible output of information to user 100, for example, as part of an augmented reality display projected onto a lens of glasses 130 or provided via a separate heads up display in communication with apparatus 110, such as a display 260 provided as part of computing device 120, which may include an onboard automobile heads up display, an augmented reality device, a virtual reality device, a smartphone, a smartwatch, PC, tablet, etc.

The term “computing device” refers to a device including a processing unit and having computing capabilities. Some examples of computing device 120 include a PC, laptop, tablet, or other computing systems such as an on-board computing system of an automobile, for example, each configured to communicate directly with apparatus 110 or server 250 over network 240. Another example of computing device 120 includes a smartphone having a display 260. In some embodiments, computing device 120 may be a computing system configured particularly for apparatus 110, and may be provided integral to apparatus 110 or tethered thereto. Apparatus 110 can also connect to computing device 120 over network 240 via any known wireless standard (e.g., Wi-Fi, Bluetooth®, etc.), as well as near-filed capacitive coupling, and other short range wireless techniques, or via a wired connection. In an embodiment in which computing device 120 is a smartphone, computing device 120 may have a dedicated application installed therein. For example, user 100 may view on display 260 data (e.g., images, video clips, extracted information, feedback information, etc.) that originate from or are triggered by apparatus 110. In addition, user 100 may select part of the data for storage in server 250.

Network 240 may be a shared, public, or private network, may encompass a wide area or local area, and may be implemented through any suitable combination of wired and/or wireless communication networks. Network 240 may further comprise an intranet or the Internet. In some embodiments, network 240 may include short range or near-field wireless communication systems for enabling communication between apparatus 110 and computing device 120 provided in close proximity to each other, such as on or near a user's person, for example. Apparatus 110 may establish a connection to network 240 autonomously, for example, using a wireless module (e.g., Wi-Fi, cellular). In some embodiments, apparatus 110 may use the wireless module when being connected to an external power source, to prolong battery life. Further, communication between apparatus 110 and server 250 may be accomplished through any suitable communication channels, such as, for example, a telephone network, an extranet, an intranet, the Internet, satellite communications, off-line communications, wireless communications, transponder communications, a local area network (LAN), a wide area network (WAN), and a virtual private network (VPN).

As shown in FIG. 2, apparatus 110 may transfer or receive data to/from server 250 via network 240. In the disclosed embodiments, the data being received from server 250 and/or computing device 120 may include numerous different types of information based on the analyzed image data, including information related to a commercial product, or a person's identity, an identified landmark, and any other information capable of being stored in or accessed by server 250. In some embodiments, data may be received and transferred via computing device 120. Server 250 and/or computing device 120 may retrieve information from different data sources (e.g., a user specific database or a user's social network account or other account, the Internet, and other managed or accessible databases) and provide information to apparatus 110 related to the analyzed image data and a recognized trigger according to the disclosed embodiments. In some embodiments, calendar-related information retrieved from the different data sources may be analyzed to provide certain time information or a time-based context for providing certain information based on the analyzed image data.

An example of wearable apparatus 110 incorporated with glasses 130 according to some embodiments (as discussed in connection with FIG. 1A) is shown in greater detail in FIG. 3A. In some embodiments, apparatus 110 may be associated with a structure (not shown in FIG. 3A) that enables easy detaching and reattaching of apparatus 110 to glasses 130. In some embodiments, when apparatus 110 attaches to glasses 130, image sensor 220 acquires a set aiming direction without the need for directional calibration. The set aiming direction of image sensor 220 may substantially coincide with the field-of-view of user 100. For example, a camera associated with image sensor 220 may be installed within apparatus 110 in a predetermined angle in a position facing slightly downwards (e.g., 5-15 degrees from the horizon). Accordingly, the set aiming direction of image sensor 220 may substantially match the field-of-view of user 100.

As further depicted in FIG. 3A, apparatus 110 may include a vibrating device 270. Vibrating device 270 may be used to provide feedback to user 100. For example, vibrating device 270 may vibrate a certain number of times (e.g., twice) to indicate that user 100 should correct one or more postures of one or more body parts and may vibrate (e.g., one vibration) to indicate that user 100 has corrected one or more postures.

FIG. 3B is an exploded view of the components of the embodiment discussed regarding FIG. 3A. Attaching apparatus 110 to glasses 130 may take place in the following way. Initially, a support 310 may be mounted on glasses 130 using a screw 320, in the side of support 310. Then, apparatus 110 may be clipped on support 310 such that it is aligned with the field-of-view of user 100. The term “support” includes any device or structure that enables detaching and reattaching of a device including a camera to a pair of glasses or to another object (e.g., a helmet). Support 310 may be made from plastic (e.g., polycarbonate), metal (e.g., aluminum), or a combination of plastic and metal (e.g., carbon fiber graphite). Support 310 may be mounted on any kind of glasses (e.g., eyeglasses, sunglasses, 3D glasses, safety glasses, etc.) using screws, bolts, snaps, or any fastening means used in the art.

In some embodiments, support 310 may include a quick release mechanism for disengaging and reengaging apparatus 110. For example, support 310 and apparatus 110 may include magnetic elements. As an alternative example, support 310 may include a male latch member and apparatus 110 may include a female receptacle. In other embodiments, support 310 can be an integral part of a pair of glasses, or sold separately and installed by an optometrist. For example, support 310 may be configured for mounting on the arms of glasses 130 near the frame front, but before the hinge. Alternatively, support 310 may be configured for mounting on the bridge of glasses 130.

In some embodiments, apparatus 110 may be provided as part of a glasses frame 130, with or without lenses. Additionally, in some embodiments, apparatus 110 may be configured to provide an augmented reality display projected onto a lens of glasses 130 (if provided), or alternatively, may include a display for projecting time information, for example, according to the disclosed embodiments. Apparatus 110 may include the additional display or alternatively, may be in communication with a separately provided display system that may or may not be attached to glasses 130.

In some embodiments, apparatus 110 may be implemented in a form other than wearable glasses, as described above with respect to FIGS. 1B-1D, for example. FIG. 4A is a schematic illustration of an example of an additional embodiment of apparatus 110 from a first viewpoint. The viewpoint shown in FIG. 4A is from the front of apparatus 110. Apparatus 110 includes an image sensor 220, a clip (not shown), a function button (not shown) and a hanging ring 410 for attaching apparatus 110 to, for example, necklace 140, as shown in FIG. 1B. When apparatus 110 hangs on necklace 140, the aiming direction of image sensor 220 may not fully coincide with the field-of-view of user 100, but the aiming direction would still correlate with the field-of-view of user 100.

FIG. 4B is a schematic illustration of the example of a second embodiment of apparatus 110, from a second viewpoint. The viewpoint shown in FIG. 4B is from a side orientation of apparatus 110. In addition to hanging ring 410, as shown in FIG. 4B, apparatus 110 may further include a clip 420. User 100 can use clip 420 to attach apparatus 110 to a shirt or belt 150, as illustrated in FIG. 1C. Clip 420 may provide an easy mechanism for disengaging and reengaging apparatus 110 from different articles of clothing. In other embodiments, apparatus 110 may include a female receptacle for connecting with a male latch of a car mount or universal stand.

In some embodiments, apparatus 110 includes a function button 430 for enabling user 100 to provide input to apparatus 110. Function button 430 may accept different types of tactile input (e.g., a tap, a click, a double-click, a long press, a right-to-left slide, a left-to-right slide). In some embodiments, each type of input may be associated with a different action. For example, a tap may be associated with the function of taking a picture, while a right-to-left slide may be associated with the function of recording a video.

The example embodiments discussed above with respect to FIGS. 3A, 3B, 4A, and 4B are not limiting. In some embodiments, apparatus 110 may be implemented in any suitable configuration for performing the disclosed methods. For example, referring back to FIG. 2, the disclosed embodiments may implement an apparatus 110 according to any configuration including an image sensor 220 and a processor unit 210 to perform image analysis and for communicating with a feedback unit 230.

FIG. 5A is a block diagram illustrating the components of apparatus 110 according to an example embodiment. As shown in FIG. 5A, and as similarly discussed above, apparatus 110 includes an image sensor 220, a memory 550, a processor 210, a feedback outputting unit 230, a wireless transceiver 530, and a mobile power source 520. In other embodiments, apparatus 110 may also include buttons, other sensors such as a microphone, and inertial measurements devices such as accelerometers, gyroscopes, magnetometers, temperature sensors, color sensors, light sensors, etc. Apparatus 110 may further include a data port 570 and a power connection 510 with suitable interfaces for connecting with an external power source or an external device (not shown).

Processor 210, depicted in FIG. 5A, may include any suitable processing device. The term “processing device” includes any physical device having an electric circuit that performs a logic operation on input or inputs. For example, processing device may include one or more integrated circuits, microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field-programmable gate array (FPGA), or other circuits suitable for executing instructions or performing logic operations. The instructions executed by the processing device may, for example, be pre-loaded into a memory integrated with or embedded into the processing device or may be stored in a separate memory (e.g., memory 550). Memory 550 may comprise a Random Access Memory (RAM), a Read-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, or volatile memory, or any other mechanism capable of storing instructions.

Although, in the embodiment illustrated in FIG. 5A, apparatus 110 includes one processing device (e.g., processor 210), apparatus 110 may include more than one processing device. Each processing device may have a similar construction, or the processing devices may be of differing constructions that are electrically connected or disconnected from each other. For example, the processing devices may be separate circuits or integrated in a single circuit. When more than one processing device is used, the processing devices may be configured to operate independently or collaboratively. The processing devices may be coupled electrically, magnetically, optically, acoustically, mechanically or by other means that permit them to interact.

In some embodiments, processor 210 may process a plurality of images captured from the environment of user 100 to determine different parameters related to capturing subsequent images. For example, processor 210 can determine, based on information derived from captured image data, a value for at least one of the following: an image resolution, a compression ratio, a cropping parameter, frame rate, a focus point, an exposure time, an aperture size, and a light sensitivity. The determined value may be used in capturing at least one subsequent image. Additionally, processor 210 can detect images including at least one posture indicator related to the user and perform an action and/or provide an output of information to a user via feedback outputting unit 230.

In another embodiment, processor 210 can change the aiming direction of image sensor 220. For example, when apparatus 110 is attached with clip 420, the aiming direction of image sensor 220 may not coincide with the field-of-view of user 100. Processor 210 may recognize certain situations from the analyzed image data and adjust the aiming direction of image sensor 220 to capture relevant image data. For example, in one embodiment, processor 210 may detect an interaction with another individual and sense that the individual is not fully in view, because image sensor 220 is tilted down. Responsive thereto, processor 210 may adjust the aiming direction of image sensor 220 to capture image data of the individual. In another example, processor 210 may detect that one or more body parts of user 100 are not fully in view. Responsive thereto, processor 210 may adjust the aiming direction of image sensor 220 to capture at least one posture indicator for the one or more body parts. Other scenarios are also contemplated where processor 210 may recognize the need to adjust an aiming direction of image sensor 220.

In some embodiments, processor 210 may communicate data to feedback outputting unit 230, which may include any device configured to provide information to a user 100. Feedback outputting unit 230 may be provided as part of apparatus 110 (as shown) or may be provided external to apparatus 110 and communicatively coupled thereto. Feedback outputting unit 230 may be configured to output visual or nonvisual feedback based on signals received from processor 210, such as when processor 210 recognizes a hand-related trigger in the analyzed image data. For example, feedback outputting unit 230 may include at least on vibrating device, as described above.

The term “feedback” refers to any output or information provided in response to processing at least one image in an environment. In some embodiments, as similarly described above, feedback may include an audible or visible indication of time information, detected text or numerals, the value of currency, a branded product, a person's identity, the identity of a landmark or other environmental situation or condition including the street names at an intersection or the color of a traffic light, etc., as well as other information associated with each of these. For example, in some embodiments, feedback may include additional information regarding the amount of currency still needed to complete a transaction, information regarding the identified person, historical information or times and prices of admission etc. of a detected landmark etc. In some embodiments, feedback may include an audible tone, a tactile response, and/or information previously recorded by user 100. Feedback outputting unit 230 may comprise appropriate components for outputting acoustical and tactile feedback. For example, feedback outputting unit 230 may comprise a vibrating device, audio headphones, a hearing aid type device, a speaker, a bone conduction headphone, interfaces that provide tactile cues, vibrotactile stimulators, etc.

In some embodiments, processor 210 may communicate signals with an external feedback outputting unit 230 via a wireless transceiver 530, a wired connection, or some other communication interface. In some embodiments, feedback outputting unit 230 may also include any suitable display device for visually displaying information to user 100.

As shown in FIG. 5A, apparatus 110 includes memory 550. Memory 550 may include one or more sets of instructions accessible to processor 210 to perform the disclosed methods, including instructions for recognizing a hand-related trigger in the image data. In some embodiments memory 550 may store image data (e.g., images, videos) captured from the environment of user 100. In addition, memory 550 may store information specific to user 100, such as image representations of known individuals, favorite products, personal items, and calendar or appointment information, etc. In some embodiments, processor 210 may determine, for example, which type of image data to store based on available storage space in memory 550. In another embodiment, processor 210 may extract information from the image data stored in memory 550.

As further shown in FIG. 5A, apparatus 110 includes mobile power source 520. The term “mobile power source” includes any device capable of providing electrical power, which can be easily carried by hand (e.g., mobile power source 520 may weigh less than a pound). The mobility of the power source enables user 100 to use apparatus 110 in a variety of situations. In some embodiments, mobile power source 520 may include one or more batteries (e.g., nickel-cadmium batteries, nickel-metal hydride batteries, and lithium-ion batteries) or any other type of electrical power supply. In other embodiments, mobile power source 520 may be rechargeable and contained within a casing that holds apparatus 110. In yet other embodiments, mobile power source 520 may include one or more energy harvesting devices for converting ambient energy into electrical energy (e.g., portable solar power units, human vibration units, etc.).

Mobile power source 520 may power one or more wireless transceivers (e.g., wireless transceiver 530 in FIG. 5A). The term “wireless transceiver” refers to any device configured to exchange transmissions over an air interface by use of radio frequency, infrared frequency, magnetic field, or electric field. Wireless transceiver 530 may use any known standard to transmit and/or receive data (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, or ZigBee). In some embodiments, wireless transceiver 530 may transmit data (e.g., raw image data, processed image data, extracted information) from apparatus 110 to computing device 120 and/or server 250. Wireless transceiver 530 may also receive data from computing device 120 and/or server 250. In other embodiments, wireless transceiver 530 may transmit data and instructions to an external feedback outputting unit 230.

FIG. 5B is a block diagram illustrating the components of apparatus 110 according to another example embodiment. In some embodiments, apparatus 110 includes a first image sensor 220 a, a second image sensor 220 b, a memory 550, a first processor 210 a, a second processor 210 b, a feedback outputting unit 230, a wireless transceiver 530, a mobile power source 520, and a power connector 510. In the arrangement shown in FIG. 5B, each of the image sensors may provide images in a different image resolution, or face a different direction. Alternatively, each image sensor may be associated with a different camera (e.g., a wide angle camera, a narrow angle camera, an IR camera, etc.). In some embodiments, apparatus 110 can select which image sensor to use based on various factors. For example, processor 210 a may determine, based on available storage space in memory 550, to capture subsequent images in a certain resolution.

Apparatus 110 may operate in a first processing-mode and in a second processing-mode, such that the first processing-mode may consume less power than the second processing-mode. For example, in the first processing-mode, apparatus 110 may capture images and process the captured images to make real-time decisions based on at least one posture indicator, for example. In the second processing-mode, apparatus 110 may extract information from stored images in memory 550 and delete images from memory 550. In some embodiments, mobile power source 520 may provide more than fifteen hours of processing in the first processing-mode and about three hours of processing in the second processing-mode. Accordingly, different processing-modes may allow mobile power source 520 to produce sufficient power for powering apparatus 110 for various time periods (e.g., more than two hours, more than four hours, more than ten hours, etc.).

In some embodiments, apparatus 110 may use first processor 210 a in the first processing-mode when powered by mobile power source 520, and second processor 210 b in the second processing-mode when powered by external power source 580 that is connectable via power connector 510. In other embodiments, apparatus 110 may determine, based on predefined conditions, which processors or which processing modes to use. Apparatus 110 may operate in the second processing-mode even when apparatus 110 is not powered by external power source 580. For example, apparatus 110 may determine that it should operate in the second processing-mode when apparatus 110 is not powered by external power source 580, if the available storage space in memory 550 for storing new image data is lower than a predefined threshold.

Although one wireless transceiver is depicted in FIG. 5B, apparatus 110 may include more than one wireless transceiver (e.g., two wireless transceivers). In an arrangement with more than one wireless transceiver, each of the wireless transceivers may use a different standard to transmit and/or receive data. In some embodiments, a first wireless transceiver may communicate with server 250 or computing device 120 using a cellular standard (e.g., LTE or GSM), and a second wireless transceiver may communicate with server 250 or computing device 120 using a short-range standard (e.g., Wi-Fi or Bluetooth®). In some embodiments, apparatus 110 may use the first wireless transceiver when the wearable apparatus is powered by a mobile power source included in the wearable apparatus, and use the second wireless transceiver when the wearable apparatus is powered by an external power source.

FIG. 5C is a block diagram illustrating the components of apparatus 110 according to another example embodiment including computing device 120. In this embodiment, apparatus 110 includes an image sensor 220, a memory 550 a, a first processor 210, a feedback outputting unit 230, a wireless transceiver 530 a, a mobile power source 520, and a power connector 510. As further shown in FIG. 5C, computing device 120 includes a processor 540, a feedback outputting unit 545, a memory 550 b, a wireless transceiver 530 b, and a display 260. One example of computing device 120 is a smartphone, smartwatch, or tablet having a dedicated application installed therein. In other embodiments, computing device 120 may include any configuration such as an on-board automobile computing system, a PC, a laptop, and any other system consistent with the disclosed embodiments. In this example, user 100 may view feedback output in response to identification of a posture indicator on display 260. Additionally, user 100 may view other data (e.g., images, video clips, object information, schedule information, extracted information, etc.) on display 260. In addition, user 100 may communicate with server 250 via computing device 120.

In some embodiments, processor 210 and processor 540 are configured to extract information from captured image data. The term “extracting information” includes any process by which information associated with objects, individuals, locations, events, etc., is identified in the captured image data by any means known to those of ordinary skill in the art. In some embodiments, apparatus 110 may use the extracted information to send feedback or other real-time indications to feedback outputting unit 230 or to computing device 120. In some embodiments, processor 210 may identify in the image data the individual standing in front of user 100, and send computing device 120 the name of the individual and the last time user 100 met the individual.

In another embodiment, processor 210 may identify in the image data, one or more visible triggers, including a hand-related trigger, and determine whether the trigger is associated with a person other than the user of the wearable apparatus to selectively determine whether to perform an action associated with the trigger. One such action may be to provide a feedback to user 100 via feedback outputting unit 230 provided as part of (or in communication with) apparatus 110 or via a feedback unit 545 provided as part of computing device 120. For example, feedback outputting unit 545 may be in communication with display 260 to cause the display 260 to visibly output information. In some embodiments, processor 210 may identify in the image data a hand-related trigger and send computing device 120 an indication of the trigger. Processor 540 may then process the received trigger information and provide an output via feedback outputting unit 545 or display 260 based on the hand-related trigger. In other embodiments, processor 540 may determine a hand-related trigger and provide suitable feedback similar to the above, based on image data received from apparatus 110. In some embodiments, processor 540 may provide instructions or other information, such as environmental information to apparatus 110 based on an identified hand-related trigger.

In some embodiments, processor 210 may identify at least one posture indicator of a body part of user 100, for example, the neck or the hands. Processor 210 and/or processor 540 may then analyze the orientation indicator and output feedback to the user with feedback outputting unit 230 and/or feedback outputting unit 260.

In some embodiments, processor 210 may identify other environmental information in the analyzed images, such as an individual standing in front user 100, and send computing device 120 information related to the analyzed information such as the name of the individual and the last time user 100 met the individual. In a different embodiment, processor 540 may extract statistical information from captured image data and forward the statistical information to server 250. For example, certain information regarding the types of items a user purchases, or the frequency a user patronizes a particular merchant, etc. may be determined by processor 540. Based on this information, server 250 may send computing device 120 coupons and discounts associated with the user's preferences.

When apparatus 110 is connected or wirelessly connected to computing device 120, apparatus 110 may transmit at least part of the image data stored in memory 550 a for storage in memory 550 b. In some embodiments, after computing device 120 confirms that transferring the part of image data was successful, processor 540 may delete the part of the image data. The term “delete” means that the image is marked as ‘deleted’ and other image data may be stored instead of it, but does not necessarily mean that the image data was physically removed from the memory.

As will be appreciated by a person skilled in the art having the benefit of this disclosure, numerous variations and/or modifications may be made to the disclosed embodiments. Not all components are essential for the operation of apparatus 110. Any component may be located in any appropriate apparatus and the components may be rearranged into a variety of configurations while providing the functionality of the disclosed embodiments. For example, in some embodiments, apparatus 110 may include a camera, a processor, and a wireless transceiver for sending data to another device. Therefore, the foregoing configurations are examples and, regardless of the configurations discussed above, apparatus 110 can capture, store, and/or process images.

Further, the foregoing and following description refers to storing and/or processing images or image data. In the embodiments disclosed herein, the stored and/or processed images or image data may comprise a representation of one or more images captured by image sensor 220. As the term is used herein, a “representation” of an image (or image data) may include an entire image or a portion of an image. A representation of an image (or image data) may have the same resolution or a lower resolution as the image (or image data), and/or a representation of an image (or image data) may be altered in some respect (e.g., be compressed, have a lower resolution, have one or more colors that are altered, etc.).

For example, apparatus 110 may capture an image and store a representation of the image that is compressed as a .JPG file. As another example, apparatus 110 may capture an image in color, but store a black-and-white representation of the color image. As yet another example, apparatus 110 may capture an image and store a different representation of the image (e.g., a portion of the image). For example, apparatus 110 may store a portion of an image that includes a face of a person who appears in the image, but that does not substantially include the environment surrounding the person. Similarly, apparatus 110 may, for example, store a portion of an image that includes a product that appears in the image, but does not substantially include the environment surrounding the product. As yet another example, apparatus 110 may store a representation of an image at a reduced resolution (i.e., at a resolution that is of a lower value than that of the captured image). Storing representations of images may allow apparatus 110 to save storage space in memory 550. Furthermore, processing representations of images may allow apparatus 110 to improve processing efficiency and/or help to preserve battery life.

In addition to the above, in some embodiments, any one of apparatus 110 or computing device 120, via processor 210 or 540, may further process the captured image data to provide additional functionality to recognize objects and/or gestures and/or other information in the captured image data. In some embodiments, actions may be taken based on the identified objects, gestures, or other information. In some embodiments, processor 210 or 540 may identify in the image data, one or more visible triggers, including a hand-related trigger, and determine whether the trigger is associated with a person other than the user to determine whether to perform an action associated with the trigger. In other embodiments, processor 210 or 540 may identify in the image data, one or more posture indicators and generate feedback based on the at least one indicator.

Some embodiments of the present disclosure may include an apparatus securable to an article of clothing of a user. Such an apparatus may include two portions, connectable by a connector. A capturing unit may be designed to be worn on the outside of a user's clothing, and may include an image sensor for capturing images of a user's environment. The capturing unit may be connected to or connectable to a power unit, which may be configured to house a power source and a processing device. The capturing unit may be a small device including a camera or other device for capturing images. The capturing unit may be designed to be inconspicuous and unobtrusive, and may be configured to communicate with a power unit concealed by a user's clothing. The power unit may include bulkier aspects of the system, such as transceiver antennas, at least one battery, a processing device, etc. In some embodiments, communication between the capturing unit and the power unit may be provided by a data cable included in the connector, while in other embodiments, communication may be wirelessly achieved between the capturing unit and the power unit. Some embodiments may permit alteration of the orientation of an image sensor of the capture unit, for example to better capture images of interest.

An apparatus consistent with embodiments of the present disclosure may also include at least one vibrating device. For example, the vibrating device may be integrated with the wearable apparatus and/or may be housed in a unit separate from and in communication with the wearable apparatus (e.g., a smartphone, a smartwatch, a tablet, or the like).

FIG. 6 illustrates an exemplary embodiment of a memory containing software modules consistent with the present disclosure. Included in memory 550 are orientation identification module 601, orientation adjustment module 602, and motion tracking module 603. Modules 601, 602, 603 may contain software instructions for execution by at least one processing device, e.g., processor 210, included with a wearable apparatus. Orientation identification module 601, orientation adjustment module 602, and motion tracking module 603 may cooperate to provide orientation adjustment for a capturing unit incorporated into wireless apparatus 110.

FIG. 7 illustrates an exemplary capturing unit 710 including an orientation adjustment unit 705. Orientation adjustment unit 705 may be configured to permit the adjustment of image sensor 220. As illustrated in FIG. 7, orientation adjustment unit 705 may include an eye-ball type adjustment mechanism. In alternative embodiments, orientation adjustment unit 705 may include gimbals, adjustable stalks, pivotable mounts, and any other suitable unit for adjusting an orientation of image sensor 220.

Image sensor 220 may be configured to be movable with the head of user 100 in such a manner that an aiming direction of image sensor 220 substantially coincides with a field of view of user 100. For example, as described above, a camera associated with image sensor 220 may be installed within capturing unit 710 at a predetermined angle in a position facing slightly upwards or downwards, depending on an intended location of capturing unit 710. Accordingly, the set aiming direction of image sensor 220 may match the field-of-view of user 100. In some embodiments, processor 210 may change the orientation of image sensor 220 using image data provided from image sensor 220. For example, processor 210 may recognize that a user is reading a book and determine that the aiming direction of image sensor 220 is offset from the text. That is, because the words in the beginning of each line of text are not fully in view, processor 210 may determine that image sensor 220 is tilted in the wrong direction. Responsive thereto, processor 210 may adjust the aiming direction of image sensor 220.

By way of further example, image sensor 220 may be configured to be movable with one or more body parts of user 100 in such a manner that the aiming direction of image sensor 220 substantially coincides with the one or more body parts. For example, as described above, a camera associated with image sensor 220 may be installed within capturing unit 710 at a predetermined angle in a position facing slightly upwards or downwards, depending on an intended location of capturing unit 710. Accordingly, the set aiming direction of image sensor 220 may be aimed at one or more body parts of user 100. In some embodiments, processor 210 may change the orientation of image sensor 220 using image data provided from image sensor 220. For example, processor 210 may recognize that one or more body parts of user 100 has moved and determine that the aiming direction of image sensor 220 is offset from the one or more body parts. That is, because a body part is not fully in view, processor 210 may determine that image sensor 220 is tilted in the wrong direction. Responsive thereto, processor 210 may adjust the aiming direction of image sensor 220.

Orientation identification module 601 may be configured to identify an orientation of an image sensor 220 of capturing unit 710. An orientation of an image sensor 220 may be identified, for example, by analysis of images captured by image sensor 220 of capturing unit 710, by tilt or attitude sensing devices within capturing unit 710, and by measuring a relative direction of orientation adjustment unit 705 with respect to the remainder of capturing unit 710.

Orientation adjustment module 602 may be configured to adjust an orientation of image sensor 220 of capturing unit 710. As discussed above, image sensor 220 may be mounted on an orientation adjustment unit 705 configured for movement. Orientation adjustment unit 705 may be configured for rotational and/or lateral movement in response to commands from orientation adjustment module 602. In some embodiments orientation adjustment unit 705 may be adjust an orientation of image sensor 220 via motors, electromagnets, permanent magnets, and/or any suitable combination thereof.

In some embodiments, monitoring module 603 may be provided for continuous monitoring. Such continuous monitoring may include tracking a movement of at least a portion of an object included in one or more images captured by the image sensor. For example, in one embodiment, apparatus 110 may track an object as long as the object remains substantially within the field-of-view of image sensor 220. In additional embodiments, monitoring module 603 may engage orientation adjustment module 602 to instruct orientation adjustment unit 705 to continually orient image sensor 220 towards an object of interest. For example, in one embodiment, monitoring module 603 may cause image sensor 220 to adjust an orientation to ensure that a certain designated object, for example, the face of a particular person or a body part of user 100, remains within the field-of view of image sensor 220, even as that designated object moves about. In another embodiment, monitoring module 603 may continuously monitor an area of interest included in one or more images captured by the image sensor. For example, a user may be occupied by a certain task, for example, typing on a laptop, while image sensor 220 remains oriented in a particular direction and continuously monitors a portion of each image from a series of images to detect a trigger or other event. For example, image sensor 210 may be oriented towards a piece of laboratory equipment and monitoring module 603 may be configured to monitor a status light on the laboratory equipment for a change in status, while the user's attention is otherwise occupied.

In some embodiments consistent with the present disclosure, capturing unit 710 may include a plurality of image sensors 220. The plurality of image sensors 220 may each be configured to capture different image data. For example, when a plurality of image sensors 220 are provided, the image sensors 220 may capture images having different resolutions, may capture wider or narrower fields of view, and may have different levels of magnification. Image sensors 220 may be provided with varying lenses to permit these different configurations. In some embodiments, a plurality of image sensors 220 may include image sensors 220 having different orientations. Thus, each of the plurality of image sensors 220 may be pointed in a different direction to capture different images. The fields of view of image sensors 220 may be overlapping in some embodiments. The plurality of image sensors 220 may each be configured for orientation adjustment, for example, by being paired with an image adjustment unit 705. In some embodiments, monitoring module 603, or another module associated with memory 550, may be configured to individually adjust the orientations of the plurality of image sensors 220 as well as to turn each of the plurality of image sensors 220 on or off as may be required. In some embodiments, monitoring an object or person captured by an image sensor 220 may include tracking movement of the object across the fields of view of the plurality of image sensors 220.

Embodiments consistent with the present disclosure may include connectors configured to connect a capturing unit and a power unit of a wearable apparatus. Capturing units consistent with the present disclosure may include least one image sensor configured to capture images of an environment of a user. Power units consistent with the present disclosure may be configured to house a power source and/or at least one processing device. Connectors consistent with the present disclosure may be configured to connect the capturing unit and the power unit, and may be configured to secure the apparatus to an article of clothing such that the capturing unit is positioned over an outer surface of the article of clothing and the power unit is positioned under an inner surface of the article of clothing. Exemplary embodiments of capturing units, connectors, and power units consistent with the disclosure are discussed in further detail with respect to FIGS. 8-14.

FIG. 8 is a schematic illustration of an embodiment of wearable apparatus 110 securable to an article of clothing consistent with the present disclosure. As illustrated in FIG. 8, capturing unit 710 and power unit 720 may be connected by a connector 730 such that capturing unit 710 is positioned on one side of an article of clothing 750 and power unit 720 is positioned on the opposite side of the clothing 750. In some embodiments, capturing unit 710 may be positioned over an outer surface of the article of clothing 750 and power unit 720 may be located under an inner surface of the article of clothing 750. The power unit 720 may be configured to be placed against the skin of a user.

Capturing unit 710 may include an image sensor 220 and an orientation adjustment unit 705 (as illustrated in FIG. 7). Power unit 720 may include mobile power source 520 and processor 210. Power unit 720 may further include any combination of elements previously discussed that may be a part of wearable apparatus 110, including, but not limited to, wireless transceiver 530, feedback outputting unit 230 (which may include one or more vibrating devices), memory 550, and data port 570.

Connector 730 may include a clip 715 or other mechanical connection designed to clip or attach capturing unit 710 and power unit 720 to an article of clothing 750 as illustrated in FIG. 8. As illustrated, clip 715 may connect to each of capturing unit 710 and power unit 720 at a perimeter thereof, and may wrap around an edge of the article of clothing 750 to affix the capturing unit 710 and power unit 720 in place. Connector 730 may further include a power cable 760 and a data cable 770. Power cable 760 may be capable of conveying power from mobile power source 520 to image sensor 220 of capturing unit 710. Power cable 760 may also be configured to provide power to any other elements of capturing unit 710, e.g., orientation adjustment unit 705. Data cable 770 may be capable of conveying captured image data from image sensor 220 in capturing unit 710 to processor 800 in the power unit 720 and may be capable of conveying feedback signals from processor 800 to one or more vibrating devices (not shown). Data cable 770 may be further capable of conveying additional data between capturing unit 710 and processor 800, e.g., control instructions for orientation adjustment unit 705.

FIG. 9 is a schematic illustration of a user 100 wearing a wearable apparatus 110 consistent with an embodiment of the present disclosure. As illustrated in FIG. 9, capturing unit 710 is located on an exterior surface of the clothing 750 of user 100. Capturing unit 710 is connected to power unit 720 (not seen in this illustration) via connector 730, which wraps around an edge of clothing 750.

In some embodiments, connector 730 may include a flexible printed circuit board (PCB). FIG. 10 illustrates an exemplary embodiment wherein connector 730 includes a flexible printed circuit board 765. Flexible printed circuit board 765 may include data connections and power connections between capturing unit 710 and power unit 720. Thus, in some embodiments, flexible printed circuit board 765 may serve to replace power cable 760 and data cable 770. In alternative embodiments, flexible printed circuit board 765 may be included in addition to at least one of power cable 760 and data cable 770. In various embodiments discussed herein, flexible printed circuit board 765 may be substituted for, or included in addition to, power cable 760 and data cable 770.

FIG. 11 is a schematic illustration of another embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure. As illustrated in FIG. 11, connector 730 may be centrally located with respect to capturing unit 710 and power unit 720. Central location of connector 730 may facilitate affixing apparatus 110 to clothing 750 through a hole in clothing 750 such as, for example, a button-hole in an existing article of clothing 750 or a specialty hole in an article of clothing 750 designed to accommodate wearable apparatus 110.

FIG. 12 is a schematic illustration of still another embodiment of wearable apparatus 110 securable to an article of clothing. As illustrated in FIG. 12, connector 730 may include a first magnet 731 and a second magnet 732. First magnet 731 and second magnet 732 may secure capturing unit 710 to power unit 720 with the article of clothing positioned between first magnet 731 and second magnet 732. In embodiments including first magnet 731 and second magnet 732, power cable 760 and data cable 770 may also be included. In these embodiments, power cable 760 and data cable 770 may be of any length, and may provide a flexible power and data connection between capturing unit 710 and power unit 720. Embodiments including first magnet 731 and second magnet 732 may further include a flexible PCB 765 connection in addition to or instead of power cable 760 and/or data cable 770. In some embodiments, first magnet 731 or second magnet 732 may be replaced by an object comprising a metal material.

FIG. 13 is a schematic illustration of yet another embodiment of a wearable apparatus 110 securable to an article of clothing. FIG. 13 illustrates an embodiment wherein power and data may be wirelessly transferred between capturing unit 710 and power unit 720. As illustrated in FIG. 13, first magnet 731 and second magnet 732 may be provided as connector 730 to secure capturing unit 710 and power unit 720 to an article of clothing 750. Power and/or data may be transferred between capturing unit 710 and power unit 720 via any suitable wireless technology, for example, magnetic and/or capacitive coupling, near field communication technologies, radiofrequency transfer, and any other wireless technology suitable for transferring data and/or power across short distances.

FIG. 14 illustrates still another embodiment of wearable apparatus 110 securable to an article of clothing 750 of a user. As illustrated in FIG. 14, connector 730 may include features designed for a contact fit. For example, capturing unit 710 may include a ring 733 with a hollow center having a diameter slightly larger than a disk-shaped protrusion 734 located on power unit 720. When pressed together with fabric of an article of clothing 750 between them, disk-shaped protrusion 734 may fit tightly inside ring 733, securing capturing unit 710 to power unit 720. FIG. 14 illustrates an embodiment that does not include any cabling or other physical connection between capturing unit 710 and power unit 720. In this embodiment, capturing unit 710 and power unit 720 may transfer power and data wirelessly. In alternative embodiments, capturing unit 710 and power unit 720 may transfer power and data via at least one of cable 760, data cable 770, and flexible printed circuit board 765.

FIG. 15 illustrates another aspect of power unit 720 consistent with embodiments described herein. Power unit 720 may be configured to be positioned directly against the user's skin. To facilitate such positioning, power unit 720 may further include at least one surface coated with a biocompatible material 740. Biocompatible materials 740 may include materials that will not negatively react with the skin of the user when worn against the skin for extended periods of time. Such materials may include, for example, silicone, PTFE, kapton, polyimide, titanium, nitinol, platinum, and others. Also as illustrated in FIG. 15, power unit 720 may be sized such that an inner volume of the power unit is substantially filled by mobile power source 520. That is, in some embodiments, the inner volume of power unit 720 may be such that the volume does not accommodate any additional components except for mobile power source 520. In some embodiments, mobile power source 520 may take advantage of its close proximity to the skin of user's skin. For example, mobile power source 520 may use the Peltier effect to produce power and/or charge the power source.

In further embodiments, an apparatus securable to an article of clothing may further include protective circuitry associated with power source 520 housed in power unit 720. FIG. 16 illustrates an exemplary embodiment including protective circuitry 775. As illustrated in FIG. 16, protective circuitry 775 may be located remotely with respect to power unit 720. In alternative embodiments, protective circuitry 775 may also be located in capturing unit 710, on flexible printed circuit board 765, or in power unit 720.

Protective circuitry 775 may be configured to protect image sensor 220 and/or other elements of capturing unit 710 from potentially dangerous currents and/or voltages produced by mobile power source 520. Protective circuitry 775 may include passive components such as capacitors, resistors, diodes, inductors, etc., to provide protection to elements of capturing unit 710. In some embodiments, protective circuitry 775 may also include active components, such as transistors, to provide protection to elements of capturing unit 710. For example, in some embodiments, protective circuitry 775 may comprise one or more resistors serving as fuses. Each fuse may comprise a wire or strip that melts (thereby braking a connection between circuitry of image capturing unit 710 and circuitry of power unit 720) when current flowing through the fuse exceeds a predetermined limit (e.g., 500 milliamps, 900 milliamps, 1 amp, 1.1 amps, 2 amp, 2.1 amps, 3 amps, etc.) Any or all of the previously described embodiments may incorporate protective circuitry 775. In some embodiments, protective circuitry 775 may further include one or more vibrating devices.

In some embodiments, the wearable apparatus may transmit data to a computing device (e.g., a smartphone, tablet, smartwatch, computer, etc.) over one or more networks via any known wireless standard (e.g., cellular, Wi-Fi, Bluetooth®, etc.), or via near-filed capacitive coupling, other short range wireless techniques, or via a wired connection. Similarly, the wearable apparatus may receive data from the computing device over one or more networks via any known wireless standard (e.g., cellular, Wi-Fi, Bluetooth®, etc.), or via near-filed capacitive coupling, other short range wireless techniques, or via a wired connection. The data transmitted to the wearable apparatus and/or received by the wireless apparatus may include images, portions of images, identifiers related to information appearing in analyzed images or associated with analyzed audio, or any other data representing image and/or audio data. For example, an image may be analyzed and an identifier related to an activity occurring in the image may be transmitted to the computing device (e.g., the “paired device”). In the embodiments described herein, the wearable apparatus may process images and/or audio locally (on board the wearable apparatus) and/or remotely (via a computing device).

Further, in some embodiments, the data transmitted to and/or received by the computing device may include one or more feedback signals. In such embodiments, the computing device may include a feedback outputting unit, which may, for example, include at least one vibrating device. Accordingly, the computing device may receive the one or more feedback signals and provide the feedback based on the received signals to the user via the feedback outputting unit.

Further, in the embodiments described herein, the wearable apparatus may transmit data related to the analysis of images and/or audio to a computing device for further analysis, display, and/or transmission to another device (e.g., a paired device). Further, a paired device may execute one or more applications (apps) to process, display, and/or analyze data (e.g., identifiers, text, images, audio, etc.) received from the wearable apparatus.

Some of the disclosed embodiments may involve systems, devices, methods, and software products for determining at least one keyword. For example, at least one keyword may be determined based on data collected by apparatus 110. At least one search query may be determined based on the at least one keyword. The at least one search query may be transmitted to a search engine.

In some embodiments, at least one keyword may be determined based on at least one or more images captured by image sensor 220. In some cases, the at least one keyword may be selected from a keywords pool stored in memory. In some cases, optical character recognition (OCR) may be performed on at least one image captured by image sensor 220, and the at least one keyword may be determined based on the OCR result. In some cases, at least one image captured by image sensor 220 may be analyzed to recognize: a person, an object, a location, a scene, and so forth. Further, the at least one keyword may be determined based on the recognized person, object, location, scene, etc. For example, the at least one keyword may comprise: a person's name, an object's name, a place's name, a date, a sport team's name, a movie's name, a book's name, and so forth.

In some embodiments, at least one keyword may be determined based on the user's behavior. The user's behavior may be determined based on an analysis of the one or more images captured by image sensor 220. In some embodiments, at least one keyword may be determined based on activities of a user and/or other person. The one or more images captured by image sensor 220 may be analyzed to identify the activities of the user and/or the other person who appears in one or more images captured by image sensor 220. In some embodiments, at least one keyword may be determined based on at least one or more audio segments captured by apparatus 110. In some embodiments, at least one keyword may be determined based on at least GPS information associated with the user. In some embodiments, at least one keyword may be determined based on at least the current time and/or date.

In some embodiments, at least one search query may be determined based on at least one keyword. In some cases, the at least one search query may comprise the at least one keyword. In some cases, the at least one search query may comprise the at least one keyword and additional keywords provided by the user. In some cases, the at least one search query may comprise the at least one keyword and one or more images, such as images captured by image sensor 220. In some cases, the at least one search query may comprise the at least one keyword and one or more audio segments, such as audio segments captured by apparatus 110.

In some embodiments, the at least one search query may be transmitted to a search engine. In some embodiments, search results provided by the search engine in response to the at least one search query may be provided to the user. In some embodiments, the at least one search query may be used to access a database.

For example, in one embodiment, the keywords may include a name of a type of food, such as quinoa, or a brand name of a food product; and the search will output information related to desirable quantities of consumption, facts about the nutritional profile, and so forth. In another example, in one embodiment, the keywords may include a name of a restaurant, and the search will output information related to the restaurant, such as a menu, opening hours, reviews, and so forth. The name of the restaurant may be obtained using OCR on an image of signage, using GPS information, and so forth. In another example, in one embodiment, the keywords may include a name of a person, and the search will provide information from a social network profile of the person. The name of the person may be obtained using OCR on an image of a name tag attached to the person's shirt, using face recognition algorithms, and so forth. In another example, in one embodiment, the keywords may include a name of a book, and the search will output information related to the book, such as reviews, sales statistics, information regarding the author of the book, and so forth. In another example, in one embodiment, the keywords may include a name of a movie, and the search will output information related to the movie, such as reviews, box office statistics, information regarding the cast of the movie, show times, and so forth. In another example, in one embodiment, the keywords may include a name of a sport team, and the search will output information related to the sport team, such as statistics, latest results, future schedule, information regarding the players of the sport team, and so forth. For example, the name of the sport team may be obtained using audio recognition algorithms.

In some embodiments, wearable apparatus 110 may monitor and/or provide feedback related to the posture of a user. For example, wearable apparatus 110 may monitor the posture of the user or of an identified body part of the user through analysis of at least one image captured by, for example, image sensor 220. Further, the at least one image captured by image sensor 220 may be analyzed to recognize at least one posture indicator. For example, the image may be analyzed for one or more known indicators, as discussed above. By way of further example, the image may be analyzed to identify at least one body part, which is then assessed for one or more posture indicators.

FIG. 17 illustrates an exemplary embodiment of a memory containing software modules consistent with the present disclosure. Included in memory 1700 are an image analysis module 1701, a feedback determination module 1702, and a feedback output module 1703. Modules 1701, 1702, and 1703 may contain software instructions for execution by at least one processing device, e.g., processor 210, included with a wearable apparatus. Image analysis module 1701, feedback determination module 1702, and feedback output module 1703 may cooperate to monitor posture of a user and provide feedback related thereto. Memory 1700 may be separate from and/or integrated with memory 550 of FIG. 6, described above. Similarly, orientation identification module 601, orientation adjustment module 602, and motion tracking module 603 of FIG. 6 may operate in tandem or concurrently with image analysis module 1701, feedback determination module 1702, and feedback output module 1703 of FIG. 17.

Image analysis module 1701 may be configured to identify at least one posture indicator from one or more images. The at least one posture indicator may be associated with the posture of a single hand of the user, of both hands of the user, of part of a single arm of the user, of parts of both arms of the user, of a single arm of the user, of both arms of the user, of the head of the user, of parts of the head of the user, of the entire body of the user, and so forth. A posture indicator may be identified, for example, by analyzing one or more images for a known indicator. For example, with respect to a hand, a known indicator may include the position of a knuckle, the contour of a finger, the outline of the hand, or the like. By way of further example, with respect to the neck, a known indicator may include the contour of the throat, the outline of a side of the neck, or the like. Image analysis module 1701 may also have a machine analysis algorithm incorporated such that a library of known indicators is updated each time image analysis module 1701 is used.

In some embodiments, one or more posture recognition algorithms may be used. For example, image analysis module 1701 may use appearance based algorithms, template matching based algorithms, deformable templates based algorithms, skeletal based algorithms, 3D models based algorithms, detection based algorithms, active shapes based algorithms, principal component analysis based algorithms, linear fingertip models based algorithms, causal analysis based algorithms, machine learning based algorithms, neural networks based algorithms, hidden Markov models based algorithms, vector analysis based algorithms, model free algorithms, indirect models algorithms, direct models algorithms, static recognition algorithms, dynamic recognition algorithms, and so forth.

Feedback determination module 1702 may be configured to generate feedback based on the at least one posture indicator. For example, the feedback may be generated based on comparison to known postures. Feedback determination module 1702 may also have a machine analysis algorithm incorporated such that a library of known postures is updated each time image analysis module 1702 is used.

Feedback output module 1703 may provide feedback output to a feedback outputting unit. For example, feedback output module 1703 may output audio feedback if feedback outputting unit includes one or more speakers, may output vibrational feedback if feedback outputting unit includes at least one vibrating device, may output visual feedback if feedback outputting unit includes at least one screen, etc.

FIG. 18 illustrates still another embodiment of wearable apparatus 110 securable to an article of clothing of a user. As illustrated in FIG. 18, capturing unit 710 includes image sensor 220 and is connected to power unit 720 by one or more hinges, e.g., hinge 1810, such that capturing unit 710 is positioned on one side of an article of clothing and power unit 720 is positioned on the opposite side of the clothing. Power unit 720 may include a plug 1820 configured to receive a cable for transferring data and/or power to apparatus 110. In some embodiments, wearable apparatus 110 may further include one or more speakers (not shown). Further, in some embodiments, wearable apparatus 110 may contain all its components inside a housing.

FIG. 19 illustrates an example of user 100 wearing apparatus 110 of FIG. 18. As illustrated in FIG. 19, the aiming direction of wearable apparatus 110 may include one or more body parts of user 100. For example, in FIG. 19, apparatus 110 is aimed to view, at least in part, the hands of user 100. As depicted, user 100 is typing on a keyboard that includes a plurality of keys. Image sensor 220 may comprise a wide-angle camera or other wide angle image sensor to facilitate the wide point-of-view of apparatus 110 depicted in FIG. 19.

FIG. 20 is a schematic illustration of a point-of-view 2010 of wearable apparatus 110 depicted in FIG. 19. As illustrated in FIG. 20, point-of-view 2010 includes the hands of user 100 and the keyboard and its keys. From point-of-view 2010, image sensor 220 may capture one or more images, which a processor of apparatus 110 may then analyze for one or more posture indicators of the hands of user 100. For example, the processor may identify the position of one or more knuckles, the contour of one or more fingers, the outline of a hand, or the like as a posture indicator of the hands of user 100. Further, the processor may identify the position of one or more knuckles, the contour of one or more fingers, the outline of a hand, or the like relative to the keyboard and/or one or more keys of the keyboard. Although not depicted in FIG. 20, in some embodiments, image sensor 220 may instead capture images include the neck of user 100 and then analyze for one or more posture indicators. For example, the processor may identify the contour of the throat, the outline of at least one side of the neck, or the like as a posture indicator of the neck of user 100.

In some embodiments, wearable apparatus 110 may use one or more image analysis techniques, such as image enhancement, edge detection, or data extraction. For example, wearable apparatus 110 may apply the image analysis techniques to detect one or more features and/or body parts (e.g., a finger, a hand, a head, a neck, a chin, a shoulder, etc.) in one or more images. Further, wearable apparatus 110 may determine certain relationships between positions of body parts and/or features in an image (e.g., a position or a distance of a finger relative to an object, such as a keyboard).

FIG. 21 is a schematic illustration of exemplary visual feedback 2110 provided to the user. As illustrated in FIG. 21, feedback 2110 may include a depiction 2120 of the posture of a body part of the user (in this example, the hands) and may further include a depiction 2130 of a recommended posture of the same body part. Accordingly, in the example of FIG. 21, feedback 2110 comprises a recommendation to the user to correct the user's typing posture from depiction 2120 to depiction 2130. In some embodiments, such visual feedback may be output to user 100 via a device paired with wearable device 110 (e.g., a smartphone, a smartwatch, a tablet, a PC, a laptop, etc.).

FIG. 22 is a schematic illustration of exemplary visual feedback 2210 provided to the user. Similar to the example shown in FIG. 21, feedback 2210 includes a depiction 2220 of the posture of a body part of the user (in this example, the hands) and a depiction 2230 of a recommended posture of the same body part. Accordingly, in the example of FIG. 22, as in the example of FIG. 21, feedback 2210 comprises a recommendation to the user to correct the user's typing posture from depiction 2220 to depiction 2230. As discussed in connection with FIG. 21, in some embodiments, such visual feedback may be output to user 100 via a device paired with wearable device 110 (e.g., a smartphone, a smartwatch, a tablet, a PC, a laptop, etc.).

One skilled in the art would understand that FIGS. 21 and 22 are examples and that other kinds of visual feedback are possible. One skilled in the art would also understand that the provided feedback may relate, not only to hands, but to any other body part, for example, the neck, an arm, the head, and so forth. Moreover, one skilled in the art would understand that the disclosed embodiments are not limited to visual feedback but rather may additionally or alternatively include audio feedback, tactile feedback, and the like.

FIG. 23 is a flowchart of a method 2300 for monitoring posture of and providing feedback to a user. Method 2300 may be implemented by a general-purpose computer or a special-purpose computer built according to embodiments of the present disclosure. For example, method 2300 may be executed by at least one of processors 210, 210 a, 210 b, and 540. In some embodiments, the steps of method 2300 may be performed by wearable apparatus 110. In other embodiments, the steps of method 2300 may be performed by wearable apparatus 110 and one or more external devices (e.g., a processor included in an external server that receives data from wearable apparatus 110 over a network and/or a processor included in an external device such as a laptop, smartwatch, smartphone, tablet, or ear phones, etc.).

At step 2310, a camera included in wearable apparatus 110 may capture one or more images from an environment. The environment may include anything within visual perception of a user of wearable apparatus 110. For example, wearable apparatus 110 may use at least one image sensor, e.g., image sensor 220, to capture the one or more images.

At step 2320, wearable apparatus 110 may analyze the one or more images to determine at least one indicator of posture of the user. For example, wearable apparatus 110 may determine at least one indicator of posture for a body part, e.g., the hands or the neck, of the user, as described above.

At step 2330, wearable apparatus 110 may determine feedback for the user based on the at least one indicator of posture. For example, wearable apparatus 110 may classify the posture information represented by the at least one indicator into at least one of a plurality of categories using a classification rule. Feedback may be generated if wearable apparatus 110 detects postures with associated posture information that is classified into one or more of the plurality of categories. Other postures with associated posture information that is classified into other categories may result in no feedback being generated.

In some embodiments, the feedback may include information associated with the category into which the posture information is classified. For example, the classification rule may be a result of training a machine learning algorithm on training examples. Examples of such machine learning algorithms includes support vector machines, Fisher's linear discriminant, nearest neighbor, k nearest neighbors, decision trees, random forests, neural networks, and so forth. By way of further example, the classification rule may be a heuristic classification rule.

In some embodiments, the feedback may include one or more recommendations for a change in the posture of the user. For example, if the processor determines that a posture of the user is an impolite posture and/or offending posture, the processor may generate feedback indicating the identification of the impolite posture and/or offending posture of the user and/or including recommendations on how to improve and/or correct the posture. By way of further example, if the processor determines that a posture of the user is a poor posture, the processor may generate feedback indicating the identification of the poor posture to the user and/or including recommendations on how to improve and/or correct the posture.

For example, feedback containing an identification of a forward head posture may also include a recommendation to slide the head backwards while preserving the line of sight. In another example, feedback containing an identification of rounded shoulders posture and/or hunchback posture may also include one or more recommendations to release chest tightness, to stretch, to perform specific workout exercises that are designed to strengthen upper back muscles, and so forth. By way of further example, feedback containing an identification of forward hilt tilt posture may also include one or more recommendations to stretch the hip flexors, to perform specific workout exercises such as glute bridges, and so forth. In another example, feedback containing an identification of over pronated feet posture may also include a recommendation to consult with a medical professional. By way of further example, feedback containing an identification of a posture that increases the risk for carpel tunnel syndrome may also include one or more recommendations and/or reminders to change the posture to a posture that does not increase the risk for carpel tunnel, one or more reports that summarizing the risk for carpel tunnel syndrome, one or more reports that summarize the occurrences of risk increasing postures over a period of time, and so forth.

In some embodiments, determining feedback for the user may include generating one or more scores associated with a posture of the user. For example, one or more scores associated with the posture of the user may be calculated based on the at least one indicator and/or based on additional posture-related information obtained by the processor. Accordingly, the processor may provide feedback and/or reports to the user based on the one or more scores. For example, if the processor identifies that the posture of the user is an impolite posture and/or offending posture, the processor may modify at least one of the one or more scores, e.g., by reducing the score(s). By way of further example, if the processor identifies that the posture of the user is a poor posture, and the processor may modify at least one of the one or more scores, e.g., by reducing the score(s). For example, if the processor identifies a forward head posture, rounded shoulders posture, hunchback posture, forward hilt tilt posture, over pronated feet posture, or the like, the processor may reduce at least one of the one or more scores. By way of further example, if the processor identifies one or more postures that increase the risk for carpel tunnel syndrome, the processor may reduce at least one of the one or more scores, while the processor may increase at least one of the one or more scores if the processor identifies one or more postures that decrease the risk for carpel tunnel syndrome.

In some embodiments, the at least one indicator and/or additional posture-related information obtained by the processor may be aggregated. In such embodiments, at least one of the one or more scores may be based on the aggregated information.

At step 2340, wearable apparatus 110 may cause the feedback to be output to the user. For example, the feedback may include audible feedback, visual feedback, tactile feedback (e.g., by causing a vibrating device or component to vibrate), or any combination thereof. The feedback may be output to the user via a speaker included in wearable apparatus 110 and/or via a device paired with wearable apparatus 110 (e.g., a smartphone, a smartwatch, a tablet, a PC, a laptop, ear phones, etc.). When device paired with wearable apparatus 110 provides the feedback, wearable apparatus 110 may transmit (e.g., via Wi-Fi over a network, e.g., the Internet, or via Bluetooth, etc.) the feedback or instructions for causing the feedback to the paired device.

Method 2300 may include additional steps. For example, in some embodiments, method 2300 may further include obtaining motion information related to the motion of the user. As used herein “motion information” may refer to information associated with the motion and/or motion pattern of the user or with a body part of the user, e.g., a single hand, both hands, part of a single arm, parts of both arms, a single arm, both arms, the head, parts of the head, and so forth.

Similar to the at least one posture indicator, the processor may analyze the one or more images to obtain the motion information. Accordingly, step 2320 may include determining at least one posture indicator and concurrently obtaining motion information. To obtain motion information, various algorithms may be used, for example, posture recognition algorithms, tracking algorithms, gesture recognition algorithms, and the like.

The generated feedback may further be based on the motion information. Accordingly, step 2330 may include determining feedback for the user based on the at least one indicator of posture and based on obtained motion information. For example, the obtained motion information may be compared to one or more received motion records. In such an example, feedback and/or reports to the user may be based on the comparison results. By way of further example, the obtained motion information may be classified into at least one of a plurality of categories using a classification rule, similar to the at least one posture indicator. In such an example, feedback may be generated if the processor obtains motion information that is classified into one or more of the plurality of categories. Other motion information that is classified into other categories may result in no feedback being generated. Moreover, the feedback may include information associated with the category into which the motion information is classified.

In some embodiments, the processor may analyze the obtained motion information to produce one or more recommendations for changes in the motion patterns of the user, and the feedback and/or reports may include and/or be based on the one or more recommendations. Accordingly, step 2330 may include determining one or more recommendations for the user based on the obtained motion information. For example, the processor may calculate one or more scores based on the obtained motion information, and the feedback and/or reports may include and/or be based on the one or more scores. By way of further example, the processor may aggregate the obtained motion information, and the feedback and/or reports may be based on the aggregated information.

FIG. 24 is a flowchart of a method 2400 for analyzing posture indicators. Method 2400 may be implemented by a general-purpose computer or a special-purpose computer built according to embodiments of the present disclosure. For example, method 2400 may be executed by at least one of processors 210, 210 a, 210 b, and 540. In some embodiments, the steps of method 2400 may be performed by wearable apparatus 110. In other embodiments, the steps of method 2400 may be performed by one or more external devices (e.g., a processor included in an external server that receives data from wearable apparatus 110 over a network and/or a processor included in an external device such as a laptop, smartwatch, smartphone, tablet, or ear phones, etc.). In yet other embodiments, the steps of method 2400 may be performed by wearable apparatus 110 and one or more external devices.

At step 2410, the processor may receive a plurality of posture-related records. For example, as described above with respect to method 2300, the processor may analyze a plurality of image data captured by the processor in order to obtain the plurality of posture-related records. In certain aspects, each posture-related record may be calculated based on different image data. For example, the different image data may have been captured at different times. The plurality of posture-related records may be stored in memory and retrieved by the processor.

At step 2420, the processor may compare posture-related information to the plurality of posture-related records. For example, the processor may analyze a plurality of image data captured by the processor in order to obtain posture-related information. In certain aspects, the posture-related information may be based on different image data than the posture-related records. For example, the posture-related information may be based on currently captured image data while the posture-related records may be based on stored image data.

In some embodiments, the processor may compare the posture-related information to the posture-related records using a similarity function. A similarity function may accept the posture-related information and one or more posture-related records as input and then output a similarly score. For example, the posture-related information and one or more posture-related records may be embedded within a mathematic space before being input to the similarity function.

In one example, each posture-related record may include information related to previously-observed posture of the user. In such an example, the processor may use the comparison results to calculate and/or update statistics about the user's posture over time. The feedback and/or reports may then include information derived from the statistics.

In another example, each posture-related record may include information related to an example posture. In such an example, the comparison results may indicate a closest or most similar example posture to the observed posture, as represented by the posture-related information. The feedback and/or reports may then be based on the identity of the closest or most similar example posture. Furthermore, the processor may calculate and/or update statistics based on the identity of the closest or most similar example posture, and the feedback and/or reports may include information derived from the statistics.

In another example, the posture-related records may include prototypes produced by applying statistical and/or machine learning algorithms, such as principal postures produced by principal component analysis, centers of cluster produced by data clustering, and so forth, on instances of information related to example postures. In such an example, the comparison results may indicate the closest or most similar prototype to the observed posture. The feedback and/or records may then be based on the identity of the closest or most similar prototype. Furthermore, the processor may calculate and/or update statistics based on the identity of the closest or most similar prototype, and the feedback and/or reports may include information derived from the statistics.

At step 2430, the processor may determine feedback for the user based on the comparison. For example, the processor may determine feedback as described above with respect to method 2300. In addition, the feedback and/or reports to the user may be based on the comparison results.

In some embodiments, feedback and/or reports to the user may be based on the plurality of posture-related records. For example, these feedback and/or reports may be based on one or more calculated scores, as described above with respect to method 2300. By way of further example, these feedback and/or reports may be based on one or more determined recommendations, as described above with respect to method 2300. By way of further example, these feedback and/or reports may be based on the classification of the posture information records into a plurality of categories, as described above with respect to method 2300.

In some embodiments, the body and/or parts of the body of one or more persons other than the user may be visible in the captured image data. In such embodiments, the processor may disregard image data related to the body and/or parts of the body of the one or more persons other than the user. Alternatively, the processor may disregard posture-related information related to the body and/or parts of the body of the one or more persons other than the user. Alternatively, the processor may disregard feedback and/or reports associated with posture-related information related to the body and/or parts of the body of the one or more persons other than the user. Accordingly, in some embodiments, additional processing to identify whether the body part is associated with the user may be performed, for example, using method 2500 described below. For example, method 2500 may be executed when the processor analyzes image data and determines that the data involves one or more persons other than the user. In embodiments in which image data related to the body and/or parts of the body of the other person(s) is discarded, the processor may then use fewer resources by withholding or forgoing any further analysis and/or determination based on the disregarded data.

FIG. 25 is a flowchart of method 2500 for monitoring posture of one or more body parts of a user. Method 2500 may be implemented by a general-purpose computer or a special-purpose computer built according to embodiments of the present disclosure. For example, method 2500 may be executed by at least one of processors 210, 210 a, 210 b, and 540. In some embodiments, the steps of method 2500 may be performed by wearable apparatus 110. In other embodiments, the steps of method 2500 may be performed by wearable apparatus 110 and one or more external devices (e.g., a processor include in an external server that receives data from wearable apparatus 110 over a network and/or a processor included in an external device such as a laptop, smartwatch, smartphone, tablet, or ear phones, etc.). In yet other embodiments, the steps of method 2500 may be performed by wearable apparatus 110 and one or more external devices.

At step 2510, a camera included in wearable apparatus 110 may capture one or more images from an environment. The environment may include anything within visual perception of a user of wearable apparatus 110. For example, wearable apparatus 110 may use at least one image sensor, e.g., image sensor 220, to capture the one or more images.

At step 2520, the processor may analyze the one or more images to identify a plurality of body parts. For example, the processor may identify body parts by comparing features of the image data (e.g., color data, contours, outlines, etc.) with known features. The processor may employ any appropriate algorithm to identify body parts and may integrate one or more machine learning techniques with such algorithm(s).

At step 2530, the processor may classify at least one of the body parts as being associated with the user. For example, various image data aspects and features may be considered for the determination, including identifying whether the body part matches that of stored images of a user's body part, identifying a context or orientation of the detected body part, assessing the environment for the presence of others, determining whether the body part is associated with another person in the field of view, determining an amount of space that the detected body part portion occupies in an image, and determining a distance of the detected body part from the processor. The processor may also make the determination based on one or more other criteria, including, for example, a confirmation or rejection signal from the user, or other external information such as an auditory signal, as well as historical information regarding an environmental situation or other learned and/or programmed assessments of the situation. In some embodiments, a determination as to whether the identified body part is associated with the user may include a sub-method comprising a first threshold determination using one technique followed by one or more addition techniques when the first threshold is satisfied, and so forth.

As described above, the determination whether a detected body part region is associated with the user may be based, at least in part, on whether the detected body part region occupies a threshold amount of space in at least one image of the analyzed image data. The threshold amount of space may, for example, be predefined or learned and programmed based on a number of test images of the associated body part of the user. For example, the predefined threshold of space that a user's body part occupies in an image to be associated with the user may be defined as at least ten percent or twenty percent (or more) of the image. Other thresholds may be used and may be based on various known characteristics of the user, such as size or age. By way of further example, a threshold amount of space may be based on the specific type of body part, e.g., a higher percentage may be used for the head than for a hand.

As described above, a size of the detected body part may also be determined to identify whether the body part is associated with the user. For example, a size of the body part may be based on the relative size of the detected body part portion in comparison to an anticipated size. By way of further example, a size of the detected body part portion may be based on the relative size of the detected body part in comparison with a known size of one or more other detected objects identified in the field of view, such as a car, a sign, a product, a tree, a landmark, the figure of a person, or the like. In such an example, the comparison may be based on known information regarding the identified objects stored in a database. By way of further example, the determination may be based on a distance of the detected body part from the user. For example, an approximate distance of the detected body part from the processor may be identified based on the comparison with known sizes of other objects. One skilled in the art would understand that there are other ways of determining a distance of a body part region away from a user consistent with the present disclosure.

The processor may also use other image processing techniques based on known parameters of the image sensor to determine the size and distance of the detected body part from the processor. For example, in embodiments in which wearable apparatus 110 includes a first image sensor (e.g., first image sensor 220 a) and a second image sensor (e.g., second image sensor 220 b), the image data from the two image sensors may be analyzed using known techniques to determine the distance of the detected body part from the processor, e.g., using binocular disparity. Other known techniques based on, for example, an infrared proximity sensor or other optical distance sensors and/or an ultrasonic distance sensor, may also be used to determine the distance of a detected object from the sensor. For example, any known sensor technology for determining distance based on a reflected or received signal is consistent with the disclosed embodiments.

The processor may further determine that a detected body part is associated with the user when the detected body part corresponds to or exceeds a threshold percentage (e.g., ten percent, twenty percent, thirty percent, forty percent, fifty percent, or more, etc.) of the entire field of view, when a size of the body part portion exceeds a size threshold, when a determined distance is less than a threshold, or any combination thereof. These techniques may also be implemented sequentially, with each other and/or with other analysis techniques (e.g., the techniques described above). For example, the processor may determine that the identified body part portion does not meet a required threshold percentage to be associated with the user and then perform no further analysis. In this example, on the other hand, if the identified body part portion meets or exceeds a threshold for being associated with the user, the processor may perform additional image analysis to confirm whether the identified body part portions are associated with the user.

Next, at step 2540, the processor may classify at least one of the body parts as being associated with the user. For example, step 2540 may further include determining whether the detected body portion matches that of stored images of a user's body. The determination may be based on identifying various features of a user's body part as may be determined based on a comparison with stored images of the user's body part and/or information associated with images of the user's body part. The stored images of the user's body part may include a plurality of images of the user's body part from various angles or in different positions. Examples of identifying features include the general appearance of the user's body part, as well as the presence of a birthmark in a particular location, a ring on a particular finger, a tattoo, a bracelet, a wristwatch, or other object typically associated with the user or characteristic identifying the user. Additional image analysis may be performed to identify a context or orientation of the detected body part portion and to determine whether such a context or orientation may be consistent with the user. For example, the processor may determine that the body part portion visible is not positioned at an orientation that would be natural for the user. Accordingly, based on the image analysis, the processor may determine that the body part portion visible is not associated with the user.

The above techniques for determining whether a user's body part is identified in the analyzed image data are exemplary only. Other similar techniques for identifying a user's body part are contemplated by the present disclosure. In some embodiments, one or more of the above techniques may be combined to determine whether an identified body part portion is associated with the user. In certain aspects, the determination may use a weighting or confidence score. For example, any one or more image analysis techniques as described above (or similar to those described) may be performed to determine whether a body part portion is associated with the user. The result of an analysis may then be associated with a weight or confidence score corresponding to the likelihood that the outcome of the analysis may be determinative. In certain aspects, a total confidence score and/or a total weight score may then be based on the results of one or more determinations. If a confidence score threshold and/or a weight score threshold is met or exceeded, the processor may determine that the identified body part portion belongs to a person other than the user.

At step 2540, the processor may analyze the at least one image of the body part associated with the user to determine at least one indicator of posture of the body part. For example, the processor may determine at least one indicator of posture as described above with respect to method 2300.

The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, Ultra HD Blu-ray, or other optical drive media.

Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. The various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets.

Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that the specification and examples be considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents. 

What is claimed is:
 1. A wearable apparatus for monitoring a posture of a user of the wearable apparatus, the wearable apparatus comprising: an image sensor configured to capture images from an environment of the user of the wearable apparatus; and at least one processor configured to: analyze one or more images of the captured images that include a representation of at least a portion of the user to determine at least one indicator of the posture of the user; analyze the one or more images to obtain motion information associated with the user; determine, based on the motion information, whether to generate feedback for the user; classify the at least one indicator of the posture of the user using a classification rule; when the at least one indicator is classified into at least one first category: generate a visual depiction of the posture of the user; generate the feedback based on the at least one indicator of the posture of the user and apply the feedback to generate a visual depiction of a recommended posture; overlay the visual depiction of the posture and the visual depiction of the recommended posture; and cause the overlaid visual depictions to be output to the user; and when the at least one indicator is classified into at least one second category: refrain from generating the feedback.
 2. The wearable apparatus of claim 1, wherein the at least one indicator of the posture of the user includes an indicator of a posture of at least one hand of the user.
 3. The wearable apparatus of claim 2, wherein the feedback includes a recommended adjustment to a position of the at least one hand of the user.
 4. The wearable apparatus of claim 1, wherein the at least one indicator of the posture of the user includes an indicator of a posture of a neck of the user.
 5. The wearable apparatus of claim 4, wherein the feedback includes a recommended adjustment to a position of the neck of the user.
 6. The wearable apparatus of claim 1, wherein the feedback to the user includes audible feedback or visual feedback.
 7. The wearable apparatus of claim 1, further comprising a vibrating component, and wherein the causing the feedback to be output to the user includes causing the vibrating component to generate a vibration.
 8. The wearable apparatus of claim 1, wherein causing the feedback to be output to the user includes transmitting the feedback to a device paired with the wearable apparatus.
 9. The wearable apparatus of claim 8, wherein the paired device includes one of a smartphone, a tablet, or a smartwatch.
 10. The wearable apparatus of claim 1, wherein the at least one processor is further configured to: receive a plurality of posture-related records; and compare the posture-related information to the plurality of posture-related records, and wherein the feedback for the user is determined based on the comparison.
 11. The wearable apparatus of claim 1, wherein the at least one first category includes a plurality of categories, and the feedback for the user is determined based on the classification of the at least one indicator of the posture of the user.
 12. The wearable apparatus of claim 11, wherein the classification rule is based on one or more machine learning algorithms trained on one or more training examples.
 13. The wearable apparatus of claim 11, wherein the classification rule is based on one or more outputs of at least one neural network.
 14. The wearable apparatus of claim 1, wherein the at least one processor is further configured to calculate a score associated with the posture of the user based on the at least one indicator of the posture of the user, and the feedback for the user is determined based on the score.
 15. The wearable apparatus of claim 1, wherein the at least one processor is further configured to: analyze the one or more images to identify a plurality of body parts; and classify at least one of the body parts as being associated with the user, and wherein analyzing the one or more images to determine the at least one indicator of the posture of the user includes analyzing at least one image of the body part associated with the user.
 16. A method for monitoring a posture of a user of a wearable apparatus, the method comprising: capturing, via an image sensor included in the wearable apparatus, one or more images that include a representation of at least a portion of the user from an environment of the user of the wearable apparatus; analyzing, via at least one processor included the wearable apparatus, the one or more images to determine at least one indicator of the posture of the user; analyzing the one or more images to obtain motion information associated with the user; determining, based on the motion information, whether to generate feedback for the user; classifying the at least one indicator of the posture of the user using a classification rule; when the at least one indicator is classified into at least one first category: generate a visual depiction of the posture of the user; generate the feedback based on the at least one indicator of the posture of the user and apply the feedback to generate a visual depiction of a recommended posture; overlay the visual depiction of the posture and the visual depiction of the recommended posture; and cause the overlaid visual depictions to be output to the user; and when the at least one indicator is classified into at least one second category: refrain from generating the feedback.
 17. The method of claim 16, wherein the at least one indicator of the posture of the user includes an indicator of a posture of at least one hand of the user.
 18. The method of claim 17, wherein the feedback includes a recommended adjustment to a position of the at least one hand of the user.
 19. The method of claim 18, wherein the at least one indicator of the posture of the user includes an indicator of a posture of a neck of the user.
 20. The method of claim 19, wherein the feedback includes a recommended adjustment to a position of the neck of the user.
 21. The method of claim 16, wherein the wearable apparatus includes a vibrating component, and causing the feedback to be output to the user includes causing the vibrating component to generate a vibration.
 22. The method of claim 16, wherein causing the feedback to be output to the user includes transmitting the feedback to a device paired with the wearable apparatus.
 23. The method of claim 16, further comprising: receiving, via the at least one processor, a plurality of posture-related records; and comparing, via the at least one processor, the posture-related information to the plurality of posture-related records, and wherein the feedback for the user is determined based on the comparison.
 24. The method of claim 16, wherein the at least one first category includes a plurality of categories, and the feedback for the user is determined based on the classification of the at least one indicator of the posture of the user.
 25. The method of claim 16, further comprising calculating, via the at least one processor, a score associated with the posture of the user based on the at least one indicator of the posture of the user, and the feedback for the user is determined based on the score.
 26. The method of claim 16, further comprising: analyzing, via the at least one processor, the one or more images to identify a plurality of body parts; and classifying, via the at least one processor, at least one of the body parts as being associated with the user, and wherein analyzing the one or more images to determine the at least one indicator of the posture of the user includes analyzing at least one image of the body part associated with the user.
 27. A non-transitory computer readable medium storing instructions for carrying out the method of claim
 16. 